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on Computational Economics |
| By: | Jinyang Li |
| Abstract: | In this research paper, we investigate into a paper named "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" [arXiv:1706.10059]. It is a portfolio management problem which is solved by deep learning techniques. The original paper proposes a financial-model-free reinforcement learning framework, which consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. Three different instants are used to realize this framework, namely a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). The performance is then examined by comparing to a number of recently reviewed or published portfolio-selection strategies. We have successfully replicated their implementations and evaluations. Besides, we further apply this framework in the stock market, instead of the cryptocurrency market that the original paper uses. The experiment in the cryptocurrency market is consistent with the original paper, which achieve superior returns. But it doesn't perform as well when applied in the stock market. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08426 |
| By: | Zian Wang; Xinyi Lu |
| Abstract: | This paper investigates the forecasting performance of COMEX copper futures realized volatility across various high-frequency intervals using both econometric volatility models and deep learning recurrent neural network models. The econometric models considered are GARCH and HAR, while the deep learning models include RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). In forecasting daily realized volatility for COMEX copper futures with a rolling window approach, the econometric models, particularly HAR, outperform recurrent neural networks overall, with HAR achieving the lowest QLIKE loss function value. However, when the data is replaced with hourly high-frequency realized volatility, the deep learning models outperform the GARCH model, and HAR attains a comparable QLIKE loss function value. Despite the black-box nature of machine learning models, the deep learning models demonstrate superior forecasting performance, surpassing the fixed QLIKE value of HAR in the experiment. Moreover, as the forecast horizon extends for daily realized volatility, deep learning models gradually close the performance gap with the GARCH model in certain loss function metrics. Nonetheless, HAR remains the most effective model overall for daily realized volatility forecasting in copper futures. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08356 |
| By: | Rademacher, Philip |
| Abstract: | This paper applies machine learning to forecast business cycles in the German economy using a high-dimensional dataset with 73 indicators, primarily from the OECD Main Economic Indicator Database, covering a time period from 1973 to 2023. Sequential Floating Forward Selection (SFFS) is used to select the most relevant indicators and build compact, explainable, and performant models. Therefore, regularized regression models (LASSO, Ridge) and tree-based classification models (Random Forest, and Logit Boost) are used as challenger models to outperform a probit model containing the term spread as a predictor. All models are trained on data from 1973-2006 and evaluated on a hold-out-sample starting in 2006. The study reveals that fewer indicators are necessary to model recessions. Models built with SFFS have a maximum of eleven indicators. Furthermore, the study setting shows that many indicators are stable across time and business cycles. Machine learning models prove particularly effective in predicting recessions during periods of quantitative easing, when the predictive power of the term spread diminishes. The findings contribute to the ongoing discussion on the use of machine learning in economic forecasting, especially in the context of limited and imbalanced data. |
| Keywords: | Business Cycles, Recession, Forecasting, Machine Learning |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:dicedp:303050 |
| By: | Daniele Ballinari; Nora Bearth |
| Abstract: | Machine learning techniques are widely used for estimating causal effects. Double/debiased machine learning (DML) (Chernozhukov et al., 2018) uses a double-robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment conditional on covariates. Estimators relying on double-robust score functions are highly sensitive to errors in propensity score predictions. Machine learners increase the severity of this problem as they tend to over- or underestimate these probabilities. Several calibration approaches have been proposed to improve probabilistic forecasts of machine learners. This paper investigates the use of probability calibration approaches within the DML framework. Simulation results demonstrate that calibrating propensity scores may significantly reduces the root mean squared error of DML estimates of the average treatment effect in finite samples. We showcase it in an empirical example and provide conditions under which calibration does not alter the asymptotic properties of the DML estimator. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.04874 |
| By: | Sario, Azhar ul Haque |
| Abstract: | This third volume in the “Stock Predictions” series builds on the success of the first edition, “Stock Price Predictions: An Introduction to Probabilistic Models” (ISBN 979-8223912712), and the second edition, “Forecasting Stock Prices: Mathematics of Probabilistic Models” (ISBN 979-8223038993). This new edition delves deeper into the complex world of quantitative finance, providing readers with a comprehensive guide to advanced financial models used in stock price prediction. The book covers a wide array of models, beginning with the foundational concept of Brownian Motion, which represents the random movement of stock prices and underpins many financial models. It then progresses to Geometric Brownian Motion, a model that accounts for the exponential growth often observed in stock prices. Mean Reversion Models are introduced to capture the tendency of stock prices to revert to their long-term average, offering a counterpoint to trend-following strategies. The book explores the world of volatility modeling with GARCH models, which capture the clustering and persistence of volatility in financial markets, crucial for risk management and option pricing. Extensions of GARCH, such as EGARCH and TGARCH, are examined to address the asymmetric impact of positive and negative news on volatility. In the latter part of the book, the focus shifts to Machine Learning, demonstrating how techniques like Support Vector Machines and Neural Networks can uncover complex patterns in financial data and enhance prediction accuracy. Recurrent Neural Networks, particularly LSTMs, are highlighted for their ability to model sequential data, making them ideal for capturing the temporal dynamics of stock prices. Monte Carlo simulations are discussed as a powerful tool for generating a range of possible future outcomes, enabling investors to assess risk and make informed decisions. Finally, Copula Models are introduced to model the dependence structure between multiple assets, critical for portfolio management and risk assessment. Throughout the book, each model is presented with a clear explanation of its mathematical formulation, parameter estimation techniques, and practical applications in stock price prediction. The book emphasizes the strengths and limitations of each model, equipping readers with the knowledge to select the most appropriate model for their specific needs. This book is an invaluable resource for students, researchers, and practitioners in finance and investments seeking to master the quantitative tools used in stock price prediction. With its rigorous yet accessible approach, this book empowers readers to leverage advanced financial models and make informed investment decisions in today’s dynamic markets. The book is based on 95 research studies, which are listed on the references page and uploaded on Harvard University’s Dataverse for transparency. As a published book, it has undergone review for originality. |
| Date: | 2024–09–13 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:pk7w3 |
| By: | Mukherjee, Krishnendu |
| Abstract: | Transportation Network Companies (TNCs) face two extreme situa-tions, namely, high demand and low demand. In high demand, TNCs use surge multiplier or surge rate to balance the high demand of riders with available drivers. Willingness of drivers, willingness of riders to pay more and appropriate surge rate play a crucial role in maximizing profits of TNCs. Otherwise, a considerable number of trips can be dis-carded either by drivers or riders. This paper explains an application of a combined classification and regression model for surge rate pre-diction. In this paper, twenty-six different machine learning (ML) al-gorithms are considered for classification and twenty-nine ML algo-rithms are considered for regression. A total of 55 ML algorithms is considered for surge rate prediction. This paper shows that estimated distance, trip price, acceptance date and time of the trip, finishing time of the trip, starting time of the trip, search radius, base price, wind velocity, humidity, wind pressure, temperature etc. determine whether surge rate or surge multiplier will be applied or not. The price per mi-nute applied for the current trip or minute price, base price, cost of the trip after inflation or deflation (i.e. trip price), the applied radius search for the trip or search radius, humidity, acceptance date of the trip with date and time, barometric pressure, wind velocity, minimum price of the trip, the price per km etc., on the other hands, influenced surge rate A case study has been discussed to implement the proposed algorithm. |
| Keywords: | Machine Learning, Surge Rate Prediction, Surge Price, Classification, Regression, Random Forest, Light GBM, XGBoost |
| JEL: | C63 C88 Y10 |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122151 |
| By: | Jingru Jia; Zehua Yuan |
| Abstract: | This study explores the potential of large language models (LLMs) to conduct market experiments, aiming to understand their capability to comprehend competitive market dynamics. We model the behavior of market agents in a controlled experimental setting, assessing their ability to converge toward competitive equilibria. The results reveal the challenges current LLMs face in replicating the dynamic decision-making processes characteristic of human trading behavior. Unlike humans, LLMs lacked the capacity to achieve market equilibrium. The research demonstrates that while LLMs provide a valuable tool for scalable and reproducible market simulations, their current limitations necessitate further advancements to fully capture the complexities of market behavior. Future work that enhances dynamic learning capabilities and incorporates elements of behavioral economics could improve the effectiveness of LLMs in the economic domain, providing new insights into market dynamics and aiding in the refinement of economic policies. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08357 |
| By: | Shengkun Wang; Taoran Ji; Linhan Wang; Yanshen Sun; Shang-Ching Liu; Amit Kumar; Chang-Tien Lu |
| Abstract: | The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language models (FinLLMs) have shown considerable progress in financial NLP tasks compared to smaller pre-trained language models (PLMs), challenges persist in stock price forecasting. Firstly, effectively integrating the modalities of time series data and natural language to fully leverage these capabilities remains complex. Secondly, FinLLMs focus more on analysis and interpretability, which can overlook the essential features of time series data. Moreover, due to the abundance of false and redundant information in financial markets, models often produce less accurate predictions when faced with such input data. In this paper, we introduce StockTime, a novel LLM-based architecture designed specifically for stock price data. Unlike recent FinLLMs, StockTime is specifically designed for stock price time series data. It leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, extracting textual information such as stock correlations, statistical trends and timestamps directly from these stock prices. StockTime then integrates both textual and time series data into the embedding space. By fusing this multimodal data, StockTime effectively predicts stock prices across arbitrary look-back periods. Our experiments demonstrate that StockTime outperforms recent LLMs, as it gives more accurate predictions while reducing memory usage and runtime costs. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08281 |
| By: | Ziyan Cui; Ning Li; Huaikang Zhou |
| Abstract: | Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical. Large Language Models (LLMs) like GPT-4 have shown promise in replicating human-like responses in various psychological experiments. However, the extent to which LLMs can effectively replace human subjects across diverse experimental contexts remains unclear. Here, we conduct a large-scale study replicating 154 psychological experiments from top social science journals with 618 main effects and 138 interaction effects using GPT-4 as a simulated participant. We find that GPT-4 successfully replicates 76.0 percent of main effects and 47.0 percent of interaction effects observed in the original studies, closely mirroring human responses in both direction and significance. However, only 19.44 percent of GPT-4's replicated confidence intervals contain the original effect sizes, with the majority of replicated effect sizes exceeding the 95 percent confidence interval of the original studies. Additionally, there is a 71.6 percent rate of unexpected significant results where the original studies reported null findings, suggesting potential overestimation or false positives. Our results demonstrate the potential of LLMs as powerful tools in psychological research but also emphasize the need for caution in interpreting AI-driven findings. While LLMs can complement human studies, they cannot yet fully replace the nuanced insights provided by human subjects. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.00128 |
| By: | Leonardo Gambacorta; Han Qiu; Shuo Shan; Daniel M Rees |
| Abstract: | In this paper we examine the effects of generative artificial intelligence (gen AI) on labour productivity. In September 2023, Ant Group introduced CodeFuse, a large language model (LLM) designed to assist programmer teams with coding. While one group of programmers used it, other programmer teams were not informed about this LLM. Leveraging this event, we conducted a field experiment on these two groups of programmers. We identified employees who used CodeFuse as the treatment group and paired them with comparable employees in the control group, to assess the impact of AI on their productivity. Our findings indicate that the use of gen AI increased code output by more than 50%. However, productivity gains are statistically significant only among entry-level or junior staff, while the impact on more senior employees is less pronounced. |
| Keywords: | artificial intelligence, productivity, field experiment, big tech |
| JEL: | D22 G31 R30 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1208 |
| By: | Bohren, Noah (University of Lausanne); Hakimov, Rustamdjan (University of Lausanne); Lalive, Rafael (University of Lausanne) |
| Abstract: | Generative artificial intelligence (AI) has made substantial progress, but some capabilities of AI are not well understood. This study compares the ability of AI to a representative population of US adults in creative and strategic tasks. The creative ideas produced by AI chatbots are rated more creative than those created by humans. Moreover, ChatGPT is substantially more creative than humans, while Bard lags behind. Augmenting humans with AI improves human creativity, albeit not as much as ideas created by ChatGPT alone. Competition from AI does not significantly reduce the creativity of men, but it decreases the creativity of women. Humans who rate the text cannot discriminate well between ideas created by AI or other humans but assign lower scores to the responses they believe to be AI-generated. As for strategic capabilities, while ChatGPT shows a clear ability to adjust its moves in a strategic game to the play of the opponent, humans are, on average, more successful in this adaptation. |
| Keywords: | artificial intelligence, ChatGPT, Bard, creativity, experiment |
| JEL: | I24 J24 D91 C90 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17302 |
| By: | Peng Zhu; Yuante Li; Yifan Hu; Qinyuan Liu; Dawei Cheng; Yuqi Liang |
| Abstract: | Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more research methods have begun to focus on exploring the interrelationships between stocks. However, existing methods mostly focus on the short-term dynamic relationships of stocks and directly integrating relationship information with temporal information. They often overlook the complex nonlinear dynamic characteristics and potential higher-order interaction relationships among stocks in the stock market. Therefore, we propose a stock price trend prediction model named LSR-IGRU in this paper, which is based on long short-term stock relationships and an improved GRU input. Firstly, we construct a long short-term relationship matrix between stocks, where secondary industry information is employed for the first time to capture long-term relationships of stocks, and overnight price information is utilized to establish short-term relationships. Next, we improve the inputs of the GRU model at each step, enabling the model to more effectively integrate temporal information and long short-term relationship information, thereby significantly improving the accuracy of predicting stock trend changes. Finally, through extensive experiments on multiple datasets from stock markets in China and the United States, we validate the superiority of the proposed LSR-IGRU model over the current state-of-the-art baseline models. We also apply the proposed model to the algorithmic trading system of a financial company, achieving significantly higher cumulative portfolio returns compared to other baseline methods. Our sources are released at https://github.com/ZP1481616577/Baseline s\_LSR-IGRU. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08282 |
| By: | Mohit Apte; Yashodhara Haribhakta |
| Abstract: | In the rapidly evolving field of financial forecasting, the application of neural networks presents a compelling advancement over traditional statistical models. This research paper explores the effectiveness of two specific neural forecasting models, N-HiTS and N-BEATS, in predicting financial market trends. Through a systematic comparison with conventional models, this study demonstrates the superior predictive capabilities of neural approaches, particularly in handling the non-linear dynamics and complex patterns inherent in financial time series data. The results indicate that N-HiTS and N-BEATS not only enhance the accuracy of forecasts but also boost the robustness and adaptability of financial predictions, offering substantial advantages in environments that require real-time decision-making. The paper concludes with insights into the practical implications of neural forecasting in financial markets and recommendations for future research directions. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.00480 |
| By: | Junjie Li; Yang Liu; Weiqing Liu; Shikai Fang; Lewen Wang; Chang Xu; Jiang Bian |
| Abstract: | Generative models aim to simulate realistic effects of various actions across different contexts, from text generation to visual effects. Despite efforts to build real-world simulators, leveraging generative models for virtual worlds, like financial markets, remains underexplored. In financial markets, generative models can simulate market effects of various behaviors, enabling interaction with market scenes and players, and training strategies without financial risk. This simulation relies on the finest structured data in financial market like orders thus building the finest realistic simulation. We propose Large Market Model (LMM), an order-level generative foundation model, for financial market simulation, akin to language modeling in the digital world. Our financial Market Simulation engine (MarS), powered by LMM, addresses the need for realistic, interactive and controllable order generation. Key objectives of this paper include evaluating LMM's scaling law in financial markets, assessing MarS's realism, balancing controlled generation with market impact, and demonstrating MarS's potential applications. We showcase MarS as a forecast tool, detection system, analysis platform, and agent training environment. Our contributions include pioneering a generative model for financial markets, designing MarS to meet domain-specific needs, and demonstrating MarS-based applications' industry potential. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.07486 |
| By: | Nasser Bouchareb (UFAS1 - Université Ferhat-Abbas Sétif 1 [Sétif]) |
| Abstract: | This paper investigates how Artificial Intelligence (AI) can improve Hotel PropertyManagement Systems (PMS) in the hospitality industry. It traces the evolution of PMS from manual to modern AI-infused counterparts, demonstrating how AI improves effectiveness and guest satisfaction. AI-driven PMS significantly improve efficiency in operations, making hotels more competitive in a dynamic landscape, by focusing on dynamic pricing strategies, real-time brand monitoring, and streamlined customer experiences. The paper highlights the practical significance of AI in the hospitality industry, promoting to a better understanding of technology's role in providing customized services and operational excellence, ultimately improving the quality of travel experiences. |
| Keywords: | Artificial Intelligence (AI) Channel manager Hospitality industry Property Management Systems (PMS) Revenue Management JEL Classification Codes: C88 L83 O32, Artificial Intelligence (AI), Channel manager, Hospitality industry, Property Management Systems (PMS), Revenue Management JEL Classification Codes: C88, L83, O32 |
| Date: | 2023–12–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04680595 |
| By: | Sandy Chen; Leqi Zeng; Abhinav Raghunathan; Flora Huang; Terrence C. Kim |
| Abstract: | Large Language Models (LLMs) research in the financial domain is particularly complex due to the sheer number of approaches proposed in literature. Retrieval-Augmented Generation (RAG) has emerged as one of the leading methods in the sector due to its inherent groundedness and data source variability. In this work, we introduce a RAG framework called Mixture of Agents (MoA) and demonstrate its viability as a practical, customizable, and highly effective approach for scaling RAG applications. MoA is essentially a layered network of individually customized small language models (Hoffmann et al., 2022) collaborating to answer questions and extract information. While there are many theoretical propositions for such an architecture and even a few libraries for generally applying the structure in practice, there are limited documented studies evaluating the potential of this framework considering real business constraints such as cost and speed. We find that the MoA framework, consisting of small language models (Hoffmann et al., 2022), produces higher quality and more grounded responses across various financial domains that are core to Vanguard's business while simultaneously maintaining low costs. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.07487 |
| By: | Yifan Jia; Yanbin Wang; Jianguo Sun; Yiwei Liu; Zhang Sheng; Ye Tian |
| Abstract: | Ethereum faces growing fraud threats. Current fraud detection methods, whether employing graph neural networks or sequence models, fail to consider the semantic information and similarity patterns within transactions. Moreover, these approaches do not leverage the potential synergistic benefits of combining both types of models. To address these challenges, we propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data in Ethereum. We first propose a transaction language model that converts numerical transaction data into meaningful transaction sentences, enabling the model to learn explicit transaction semantics. Then, we propose a transaction attribute similarity graph to learn transaction similarity information, enabling us to capture intuitive insights into transaction anomalies. Additionally, we construct an account interaction graph to capture the structural information of the account transaction network. We employ a deep multi-head attention network to fuse transaction semantic and similarity embeddings, and ultimately propose a joint training approach for the multi-head attention network and the account interaction graph to obtain the synergistic benefits of both. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.07494 |
| By: | Lo\"ic Mar\'echal; Nathan Monnet |
| Abstract: | We use a methodology based on a machine learning algorithm to quantify firms' cyber risks based on their disclosures and a dedicated cyber corpus. The model can identify paragraphs related to determined cyber-threat types and accordingly attribute several related cyber scores to the firm. The cyber scores are unrelated to other firms' characteristics. Stocks with high cyber scores significantly outperform other stocks. The long-short cyber risk factors have positive risk premia, are robust to all factors' benchmarks, and help price returns. Furthermore, we suggest the market does not distinguish between different types of cyber risks but instead views them as a single, aggregate cyber risk. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08728 |
| By: | Shuochen Bi; Yufan Lian; Ziyue Wang |
| Abstract: | In the financial field of the United States, the application of big data technology has become one of the important means for financial institutions to enhance competitiveness and reduce risks. The core objective of this article is to explore how to fully utilize big data technology to achieve complete integration of internal and external data of financial institutions, and create an efficient and reliable platform for big data collection, storage, and analysis. With the continuous expansion and innovation of financial business, traditional risk management models are no longer able to meet the increasingly complex market demands. This article adopts big data mining and real-time streaming data processing technology to monitor, analyze, and alert various business data. Through statistical analysis of historical data and precise mining of customer transaction behavior and relationships, potential risks can be more accurately identified and timely responses can be made. This article designs and implements a financial big data intelligent risk control platform. This platform not only achieves effective integration, storage, and analysis of internal and external data of financial institutions, but also intelligently displays customer characteristics and their related relationships, as well as intelligent supervision of various risk information |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.10331 |